Ashraf Radaideh

The variable nature of photovoltaic (PV) power generation presents a significant challenge for the reliable operation of standalone PV systems, demanding accurate short-term forecasting to improve energy management and describing battery utilization. This study suggests a multivariable long short-term memory (LSTM) network for 10 min ahead PV power forecasting using one year of operational data collected from an off-grid PV system with batteries at a 5 min sampling interval. The model integrates historical PV power, solar irradiance, load demand, battery current, battery voltage, and dynamics of PV generations. The proposed model achieved a daylight forecasting accuracy of R² = 0.728, demonstrating good agreement between the measured and predicted PV power profiles while effectively capturing daily generation patterns under varying conditions. Residual analysis indicated that the prediction errors were centred around zero, confirming the absence of significant systematic bias. Moreover, the annual battery state of charge (SOC) remained within the operational limits of 20–90 percent, demonstrating stable battery operation throughout the entire assessment period. Along with the PV forecasting results, these findings provide predictive and operational insights into the performance of the off-grid PV system.

In modern power systems, increasing flexibility has become an essential aspect of supporting the integration of renewable energy resources (RERs). The rapid fluctuations in power output from RERs and the reduced reliance on conventional thermal units necessitate flexible energy resources and demand‐side solutions. This study introduces a control framework that utilizes thermostatically controlled loads (TCLs) represented by electric water heaters (EWHs) to support the power system operation in delivering both primary and secondary frequency responses, especially for situations that lack adequate ramping capability from conventional resources. At the operator level, load frequency regulation is performed using a model predictive control (MPC) framework enhanced with integral action to control the frequency in a two‐area power system. The MPC is responsible for adjusting the thermal power plant outputs and providing the reference control signals for demand response agents (DRAs), which are responsible for load aggregation, monitoring, and control. DRAs based on state‐space statistical learning techniques develop an aggregate model within a linear system framework, which is used in a linear quadratic regulator (LQR) to optimally manage device operation and achieve the reference control signals. The developed approach can transform thousands of individual binary devices into a compact, fixed‐dimensional state‐space model. Therefore, the computational burden remains independent of the number of underlying devices, proving its viability for large‐scale power system implementations. This study considers 10,000 heterogeneous EWHs distributed in each control area. MATLAB simulations demonstrate that TCLs can effectively contribute to primary and secondary frequency control, offering a scalable solution for integrating TCLs into future grid operations and improving grid flexibility.

A standalone hybrid renewable energy system (HRES) that combines different types of renewable energy sources and storages offers a sustainable energy solution by reducing reliance on fossil fuels and minimizing greenhouse gas emissions. In this paper, a standalone hybrid renewable energy system (HRES) involving wind turbines, photovoltaic (PV) modules, diesel generators (DG), and battery banks is proposed. For this purpose, it is necessary to size and run the proposed system for feeding a residential load satisfactorily. For two typical winter and summer weeks, weather historical data, including irradiance, temperature, wind speed, and load profiles, are used as input data. The overall optimization framework is formulated as a bi-level mixed-integer nonlinear programming (BMINLP) problem. The upper-level part represents the sizing sub-problem that is solved based on economic and environmental multi-objectives. The lower-level part represents the energy management strategy (EMS) sub-problem. The EMS task utilizes the model predictive control (MPC) approach to achieve optimal technoeconomic operational performance. By the definition of BMINLP, the EMS sub-problem is defined within the constraints of the sizing sub-problem. The MATLAB R2023a environment is employed to execute and extract the results of the entire problem. The global optimization solver “ga” is utilized to implement the upper sub-problem while the “intlinprg” solver solves the lower sub-problem. The evaluation metrics used in this study are the operating, maintenance, and investment costs, storage unit degradation, and the number of CO₂ emissions.

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (E_D) and the total wind energy (E_T) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m² to nearly 1250 kWh/m². In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.

This paper proposes an optimal gain-scheduling for linear quadratic regulator (LQR) control framework to improve the performance of wind turbines based Doubly Fed Induction Generator (DFIG). Active and reactive power decoupling is performed using the field-oriented vector control which is used to simplify DFIG’s nonlinearity and derive a compact linearized state-space model. The performance of the optimal controller represented by a linear quadratic regulator is further enhanced using the whale optimization algorithm in a multiobjective optimization environment. Adaptiveness against wind speed variation is achieved in an offline training process at a discretized wind speed domain. Lookup tables are used to store the optimal controller parameter and called upon during the online implementation. The control framework further integrates the effects of pitch angle control mechanism for active power ancillary services and possible improvements on reactive power support. The results of the proposed control framework improve the overall performance of the system compared to the conventional PI controller. Comparison is performed using the MATLAB Simulink platform.

Photovoltaic (PV) panels installation has become one of the major technologies used for energy production worldwide. Knowledge and competitive prices are the main reasons for the spread usage and expanded exploiting of PV systems. Accordingly, this creates several challenges for manufacturers and customers, mainly, the quality of PV panels to withstand environmental conditions during service lifetime. Hence, the quality of PV panels is a vital aspect. By thinking of PV power plants, it appears that some factors should be considered, like the developing microcracks (µcracks). An issue like that increases the chances of having power loss during the operation phase. Notably, µcracks develop in different shapes and orientations; the variation depends on what causes them. This study is a presentation and summary of data collected from different projects in Jordan to describe the effect of each µcracks shape on power loss, aiming to give decision makers an indication to decide whether to replace the faulty panels or not, depending on their own conditions and projects sizes. Hence, in this study, it was found that the µcracks have impacted power loss differently and recorded power reduction of percentages of 0.82–3.21% for poly-crystalline technology. Variation in power degradation depends on the module situation; whether it is stocked in facility or operated on-site. In the mono-crystalline technology case, the power losses varied between 0.55% and 0.9%, with the exception of some samples from both technologies that have effects other than microcracks, which affected power severely. Furthermore, a general overview is provided for µcracks before installation.

This paper investigates utilizing a heterogeneous group of Thermostatically Controlled Loads (TCLs) for long-term demand response applications. The steady-state services are achieved through manipulating the stored thermal-energy with minimal impact on devices’ switching rates and the operating duty-cycles. The Markov chain abstraction method has been developed in literature for aggregating the TCLs at fixed temperature set-point. In this work, an Extended Markov Model (EMM) is proposed to account for the dynamics involved in modifying various set-point magnitudes in both directions. The EMM is formulated online based on linear mapping and fast restructuring to Markov chains developed offline at fixed set-points, where a training process is used to construct each Markov chain. Set-point adjustments force devices to operate in a synchronized pattern, causing the aggregated power to oscillate or traverse extreme conditions. Therefore, model predictive control with direct ON/OFF switching capability is proposed to apply the set-point change sequentially and control devices’ movement toward the new operating set-point. The performance of the proposed modeling and control techniques are compared against existing methods which rely on the direct ON/OFF control solely rather than adjusting the thermal-energy level.

Using a Homogeneous Control Group (Hom-CG) of Thermostatically Controlled Loads (TCLs) to extract short-term system-level services involves a serious challenging problem. Similar device characteristics forces a large number of devices to operate in synchronized patterns when subjected to common dispatch signals. Therefore, power system operators have to deal with subsequent large aggregated dynamic responses that may result in serious stability issues. This paper thus proposes a new load control mechanism for suppressing power oscillations that may result after manipulating the temperature set-point. The control methodology requires adopting advanced thermostat, which is able to adjust device's temperature set-point and deadband bounds in response to receiving the dispatching signals. The control signals could either be centralized or based on local measurements from a frequency-sensing devices. Adopting such a new control architecture will guarantee converting any group of TCLs to an eligible control group able to provide an optimal system-level ancillary services while preserving service quality within acceptable limits. The control scheme efficiency has been verified by performing several Monte-Carlo simulations for a set of 100,000 Hom-CG of TCLs representing a residential air conditioners. The simulations were performed using the MATLAB (R2015a) platform.